Azure Data lake storage (ADLS) and Azure Blob Storage:
Azure Blob Storage: Binary Large Object Storage use to store unstructured data such as text or binary data, you can store large amount of unstructured data in single hierarchy, it also known as flat name space.
Azure Data Lake storage (ADLS): Data lake solution for high performance big data analytics. It provide file system hierarchy, generally use if we want to store data as like partitioning or bucketing manage. Which help to improve performance during big data analytics. It also know as hierarchical namespace.
Hierarchical namespaces: organize blob data into directories and stores metadata about each directory and the files within it. They keep the data organized, which yields better storage and retrieval performance for an analytical use case and lowers the cost of analysis. This structure allows operations, such as directory renames and deletes, to be performed in a single atomic operation.
Blob storage is use for:
- Serving images or documents directly to a browser.
- Storing files for distributed access, such as installation.
- Streaming video and audio.
- Storing data for backup and restore, disaster recovery, and archiving.
- Writing to log files.
- Any type of text or binary data, such as application backend, backup data, and general purpose data.
ADLS is use for:
- Creating a modern data warehouse.
- Advanced analytics against big data.
- Creating a real-time analytical solution.
- Hadoop compatible access (HDFS, ABFS) is required. Access it through compute technologies including Azure Databricks, Azure HDInsight, and Azure Synapse Analytics without moving the data between environments.
- ACL and POSIX permissions along with some extra granularity support is required.
- Batch, interactive, streaming analytics, and machine learning data such as log files, IoT data, click streams, and large datasets.
